On a hybrid data cloning method and its application in generalized linear mixed models

Data cloning method is a new computational tool for computing maximum likelihood estimates in complex statistical models such as mixed models. The data cloning method is synthesized with integrated nested Laplace approximation to compute maximum likelihood estimates efficiently via a fast implementation in generalized linear mixed models. Asymptotic normality of the hybrid data cloning based distribution is established aided by modification of Stein’s Identity. The results are illustrated through a series of well known examples. It is shown that the proposed method as well as normal approximation perform very well and justify the theory.